[1] 19 27 24 27 27 24 24 22 25 22 25 31 21 23
A tour of favourite hits
I think the two must read books on data viz are Data Visualization by Kieran Healy and Fundamentals of Data Visualization by Claus Wilke.
Following is the number of time I day dream about food, recorded over two weeks:
[1] 19 27 24 27 27 24 24 22 25 22 25 31 21 23
Here is the number of times I tried to complete this presentation over the same period of time:
[1] 28 27 28 24 26 15 31 19 22 19 34 34 25 20
It is not easy and intuitive to look at number and say something about it. Even if summary stats are provided. Let us look at this more closely.
x1 x2 x3 x4 y1 y2 y3 y4
1 10 10 10 8 8.04 9.14 7.46 6.58
2 8 8 8 8 6.95 8.14 6.77 5.76
3 13 13 13 8 7.58 8.74 12.74 7.71
4 9 9 9 8 8.81 8.77 7.11 8.84
5 11 11 11 8 8.33 9.26 7.81 8.47
6 14 14 14 8 9.96 8.10 8.84 7.04
7 6 6 6 8 7.24 6.13 6.08 5.25
8 4 4 4 19 4.26 3.10 5.39 12.50
9 12 12 12 8 10.84 9.13 8.15 5.56
10 7 7 7 8 4.82 7.26 6.42 7.91
11 5 5 5 8 5.68 4.74 5.73 6.89
# A tibble: 4 × 3
variable mean sd
<chr> <dbl> <dbl>
1 y1 7.50 2.03
2 y2 7.50 2.03
3 y3 7.5 2.03
4 y4 7.50 2.03
Corr x1, y1: 0.8164205
Corr x1, y1: 0.8162365
Corr x1, y1: 0.8162867
Corr x1, y1: 0.8165214
Anscombe’s quartet-from Data Visualization by Healy
Napolean’s retreat from Russia by Minard-from Data Visualization by Healy
`Monstrous Costs’ by Nigel Holmes-from Data Visualization by Healy
Rainfall in Glasgow and Edinbrugh-from Cara Thompson’s More than pretty graphs
Rainfall in Glasgow and Edinbrugh-from Cara Thompson’s More than pretty graphs
“Graphical excellence is the well-designed presentation of interesting data—a matter of substance, of statistics, and of design … [It] consists of complex ideas communicated with clarity, precision, and efficiency. … [It] is that which gives to the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space … [It] is nearly always multivariate … And graphical excellence requires telling the truth about the data. (Tufte, 1983, p. 51).”
reference to NYT graph-from Data Visualization by Healy
The following problems are distinct but can appear in in various combinations in a given figure.
reference to NYT graph-from Data Visualization by Healy
Voeten’s response to NYT graph-from Data Visualization by Healy
Healy’s examples for possible manipulations from Data Visualization by Healy
Healy’s examples for possible manipulations from Data Visualization by Healy
Healy’s Alternative to the index vs money base chart from Data Visualization by Healy
Bin width example from Fundamentals of Data Visualization by Wilke
Incorrect data representation example from Fundamentals of Data Visualization by Wilke
Multiple Distribution common error from Fundamentals of Data Visualization by Wilke
Multiple Distribution common error from Fundamentals of Data Visualization by Wilke
for multi variable distribution
Alternatives for Multiple Distribution from Fundamentals of Data Visualization by Wilke
for multi variable distribution
Alternatives for Multiple Distribution from Fundamentals of Data Visualization by Wilke
for multi variable distribution
Alternatives for Multiple Distribution from Fundamentals of Data Visualization by Wilke
for multi variable distribution
Alternatives for Multiple Distribution from Fundamentals of Data Visualization by Wilke